Магистратура
2021/2022
Анализ ковариационных моделей
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс обязательный (Прикладная статистика с методами сетевого анализа)
Направление:
01.04.02. Прикладная математика и информатика
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Кускова Валентина Викторовна
Прогр. обучения:
Прикладная статистика с методами сетевого анализа
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
This course is designed for MASNA students who would like to acquire a significant familiarity with the statistical techniques known collectively as "structural equation modeling," "causal modeling," or "analysis of covariance structures."
Learning Objectives
- To provide you with an understanding of the basic principles of latent variable structural equation modeling and lay the foundation for future learning in the area.
- To explore the advantages and disadvantages of latent variable structural equation modeling, and how it relates to other methods of analysis.
- To develop your familiarity, through hands on experience, with the major structural equation modeling programs, so that you can use them and interpret their output.
- To develop and/or foster critical reviewing skills of published empirical research using structural equation modeling.
Expected Learning Outcomes
- Be able to use the major SEM programs to estimate common types of models: Formative indicator models.
- Be able to use the major SEM programs to estimate common types of models: Latent growth curve models, latent state-trait-occasion models, etc.
- Be able to use the major SEM programs to estimate common types of models: Latent variable multi-equation models.
- Be able to use the major SEM programs to estimate common types of models: Models with latent variable interactions.
- Be able to use the major SEM programs to estimate common types of models: Models with multiple mediating effects.
- Be able to use the major SEM programs to estimate common types of models: Multi-equation path analysis models
- Be able to use the major SEM programs to estimate common types of models: Multi-group models with mean structures.
- Be able to use the major SEM programs to estimate common types of models: Multi-level models (If time permits).
- Be able to use the major SEM programs to estimate common types of models: Path models with fixed, non-zero error terms
- Be able to use the major SEM programs to estimate common types of models: Second-order factor models.
- Have a working knowledge of the different ways to analyze models with covariance structures.
- Have an understanding common problems related to model specification, identification, and estimation.
- Know how to translate conceptual thinking into models that can be estimated.
- Know the basic idea of implied matrices and what is happening in SEM.
- Know the major structural equation modeling programs.
Course Contents
- Course Introduction
- Problem Selection and Conceptualization
- Fundamentals of LVSEM (Part 1)
- Basic Model
- Fundamentals of LVSEM (Part 2)
- Fundamentals of LVSEM (Part 3)
- Software Programs
- Observed Variable Models – Path Analysis
- Testing Mediation
- Effect Decomposition
- Measurement Model Specification
- Assessing Construct Validity and Reliability
- Multiple Groups Analysis
- Latent Variable Interactions
- Latent Change Analysis
- Special Topics
Assessment Elements
- Answers to Readings Questions
- Basics Exam
- Path Analysis and Mediating Effects
- Latent Variable Model
- Moderating Effects with Latent Variables
- Special Topic Presentation
Interim Assessment
- 2021/2022 1st module0.4 * Answers to Readings Questions + 0.6 * Basics Exam
- 2021/2022 2nd module0.25 * Latent Variable Model + 0.25 * Moderating Effects with Latent Variables + 0.25 * Path Analysis and Mediating Effects + 0.25 * Special Topic Presentation
Bibliography
Recommended Core Bibliography
- Netemeyer, R. G., Sharma, S., & Bearden, W. O. (2003). Scaling Procedures : Issues and Applications. Thousand Oaks, Calif: SAGE Publications, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=321358
- Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193
Recommended Additional Bibliography
- Byrne, B. M. (1998). Structural Equation Modeling With Lisrel, Prelis, and Simplis : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=582749
- Byrne, B. M. (2000). Structural Equation Modeling With AMOS : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=54805